个人化设计在临床研究中的应用——从个体角度研究个体差异

本文从有关研究证据的基础上,介绍了个人化设计的理论基础和实验设计,并在此基础上讨论了个人化设计在临床研究领域中的应用价值,总结出一种适合临床病人使用的研究程序。个人化设计允许对个体的个人价值进行分析,并能同时研究不同刺激信息的作用,在个体水平上分析个体差异。通过简化了的实验程序,个人化设计为解决临床研究中的问题提供了一种新的思维方式。文中还对相关研究法的局限进行了讨论。     

Multilevel models in developmental psychological research: Rationales and applications

The armoury of statistical techniques has recently been enhanced with the multilevel model for longitudinal data. The present article explains the multilevel model for longitudinal data to the statistically non-sophisticated reader. It shows how the model relates to the five rationales of longitudinal research, as they were put forward by Baltes and Nesselroade (1979). In addition, it shows how the model matches Wohlwill's methodology of developmental functions. To elucidate the model further, three applications are given. The applications pertain to the development of children's sleeping duration during the first 18 months of life, the effects of early hospitalization on the daily frequency of crying, and the developmental relationship between social and solitary play.     

Effects of modelling on children''s pretend play

The effects of modelling on pretend play were studied in children between 2.6 and 3.6 years of age by examining changes both in the action and language-based expressions of pretending. Three modelled scenes were demonstrated with the same realistic toys used by the child in the pre-modelling phase. The results gave support to the idea of the effectiveness of modelling. The effects of the modelled scenes were clearest among children whose pre-modelling play consisted of object-centred actions including few of pretending. The post-modelling play of this subgroup showed better quality of action and language categories and integration measures. Among the decentred players no significant action benefit could be achieved. This might have been an outcome of the contextual limits of the toy material and of the drop of play motivation caused by the model interruption. However, these children expressed verbal pretending revealing significant pre-post effects for inventive utterances.     

Latent trait models and dichotomization of graded responses

This paper discusses thecompatibility of the polychotomous Rasch model with dichotomization of the response continuum. It is argued that in the case of graded responses, the response categories presented to the subject are essentially an arbitrary polychotomization of the response continuum, ranging for example from total rejection or disagreement to total acceptance or agreement of an item or statement. Because of this arbitrariness, the measurement outcome should be independent of the specific polychotomization applied, for example, presenting a specific multicategory response format should not affect the measurement outcome. When such is the case, the original polychotomous model is called compatible with dichotomization.A distinction is made between polychotomization or dichotomization before the fact, that is, in constructing the response format, and polycho- or dichotomization after the fact, for example in dichotomizing existing graded response data.It is shown that, at least in case of dichotomization after-the-fact, the polychotomous Rasch model is not compatible with dichotomization, unless a rather special condition of the model parameters is met. Insofar as it may be argued that dichotomization before the fact is not essentially different from dichotomization after the fact, the value of the unidimensional polychotomous Rasch model is consequently questionable. The impact of our conclusion on related models is also discussed.     

A family of chance-corrected association coefficients for metric scales

A chance-corrected version of the family of association coefficients for metric scales proposed by Zegers and ten Berge is presented. It is shown that a matrix with chance-corrected coefficients between a number of variables is Gramian. The members of the chance-corrected family are shown to be partially ordered. The author is obliged to Jos ten Berge for helpful comments.     

The effect of the two-chair dialogue intervention on self-compassion - adding an emotional evocative component to a basic Rogerian condition

Objective

How people relate to themselves when facing distress or failure influences general psychological well-being and vulnerability to psychological disorders. The aim of the present study was to explore the effect of an emotionally evocative intervention on self-compassion.

Methods

The data were retrieved from a larger study of Emotion-Focused Therapy (EFT) utilising a multiple baseline design comparing two treatment phases. The baseline phase consisted of 5, 7 or 9 therapy sessions where the therapist solely adhered to Rogerian relational conditions, as prescribed in EFT. A two-chair dialogue intervention was then added for five sessions. The sample consisted of 18 self-critical clients with clinically significant symptoms of depression and/or anxiety. The “Self-Compassion Scale” (SCS) was administered pre, mid and post therapy.

Results

The baseline phase did not lead to significant changes in self-compassion. However, the addition of the two-chair dialogue was associated with a significant increase in self-compassion. This increase was due to reductions in the negative subscales, especially the isolation subscale.

Conclusion

The emotionally evocative two-chair dialogue was associated with a significant change in self-to-self relating, compared to relational conditions alone. The two-chair dialogue, thus, seems to be a promising intervention for promoting healthier self-to-self relating.     

Penalized optimal scaling for ordinal variables with an application to international classification of functioning core sets

Ordinal data occur frequently in the social sciences. When applying principal component analysis (PCA), however, those data are often treated as numeric, implying linear relationships between the variables at hand; alternatively, non-linear PCA is applied where the obtained quantifications are sometimes hard to interpret. Non-linear PCA for categorical data, also called optimal scoring/scaling, constructs new variables by assigning numerical values to categories such that the proportion of variance in those new variables that is explained by a predefined number of principal components (PCs) is maximized. We propose a penalized version of non-linear PCA for ordinal variables that is a smoothed intermediate between standard PCA on category labels and non-linear PCA as used so far. The new approach is by no means limited to monotonic effects and offers both better interpretability of the non-linear transformation of the category labels and better performance on validation data than unpenalized non-linear PCA and/or standard linear PCA. In particular, an application of penalized optimal scaling to ordinal data as given with the International Classification of Functioning, Disability and Health (ICF) is provided.     

Heterogeneous heterogeneity by default: Testing categorical moderators in mixed-effects meta-analysis

Categorical moderators are often included in mixed-effects meta-analysis to explain heterogeneity in effect sizes. An assumption in tests of categorical moderator effects is that of a constant between-study variance across all levels of the moderator. Although it rarely receives serious thought, there can be statistical ramifications to upholding this assumption. We propose that researchers should instead default to assuming unequal between-study variances when analysing categorical moderators. To achieve this, we suggest using a mixed-effects location-scale model (MELSM) to allow group-specific estimates for the between-study variance. In two extensive simulation studies, we show that in terms of Type I error and statistical power, little is lost by using the MELSM for moderator tests, but there can be serious costs when an equal variance mixed-effects model (MEM) is used. Most notably, in scenarios with balanced sample sizes or equal between-study variance, the Type I error and power rates are nearly identical between the MEM and the MELSM. On the other hand, with imbalanced sample sizes and unequal variances, the Type I error rate under the MEM can be grossly inflated or overly conservative, whereas the MELSM does comparatively well in controlling the Type I error across the majority of cases. A notable exception where the MELSM did not clearly outperform the MEM was in the case of few studies (e.g., 5). With respect to power, the MELSM had similar or higher power than the MEM in conditions where the latter produced non-inflated Type 1 error rates. Together, our results support the idea that assuming unequal between-study variances is preferred as a default strategy when testing categorical moderators.     

初中生学业成就动机量表的编制

本研究根据我国初中生的实际编制了一套测量初中生学业成就动机的自陈式量表。该量表由外部行为表现和内部心理因素两个分量表构成。行为表现分置表包括主动性、行为策略和坚持性三个子量表,心理因素分量表包括能力、兴趣、目的、知识价值观四个子量表。测题分析和测验分析的结果表明此量表具有较高的信度和效度,能用于鉴别初中生学业成就动机的水平。     

A generalized family of coefficients of relational agreement for numerical scales

A family of coefficients of relational agreement for numerical scales is proposed. The theory is a generalization to multiple judges of the Zegers and ten Berge theory of association coefficients for two variables and is based on the premise that the choice of a coefficient depends on the scale type of the variables, defined by the class of admissible transformations. Coefficients of relational agreement that denote agreement with respect to empirically meaningful relationships are derived for absolute, ratio, interval, and additive scales. The proposed theory is compared to intraclass correlation, and it is shown that the coefficient of additivity is identical to one measure of intraclass correlation.The author thanks the Editor and anonymous reviewers for helpful suggestions.